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e2Drive: Enhancing Urban Mobility and Safety for Physically Disabled Drivers Through Smart Sensor System 1

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16th International Conference on Ubiquitous Computing and Ambient Intelligence

作者： Bleda, Andres L. Luis Leal, Juan Angel Beteta, Miguel Maestre, Rafael Abbenante, Sergio E. Francisco Puche-Forte, Jose Technol Ctr Furniture & Wood Reg Murcia CETEM Yecla 30510 Spain

ISBN: (数字)9783031775710

ISBN: (纸本)9783031775703;9783031775710

The e2DriveR&D initiative addresses the critical need for improved urban mobility solutions for people with physical disabilities. This project involves the development of an innovative electric vehicle (EV) equipped with an intelligent driver control system, focusing particularly on the integration of a smart sensor system designed to enhance safety and mobility for drivers with reduced physical capabilities. In this article, we explore the advanced research and technological design of the smart sensor system integrated within the e2Drivevehicle. The system incorporates a range of sensors to monitor vital signs such as heart rate, breathing rhythm, eye blinking, real-time driving patterns through an On Board Diagnostics (OBD-II) sensor, and the status of the lock mechanism for the wheelchair. An associated mobile app is designed to alert predefined contacts or emergency services when anomalies are detected, thereby significantly enhancing driver safety. Tests demonstrated the system's efficacy in monitoring driving behavior and vital signs, with the alert system effectively warning of detected anomalies, confirming its potential to significantly improve the safety and health monitoring of physically disabled drivers. The huge potential of artificial intelligence (AI) techniques for detecting driving anomalies (fatigue or health issues) remains promising for future enhancements.

关键词： Urban mobility Physically disabled drivers Smart sensor system Real-time monitoring Electric vehicle Driver safety Health monitoring

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Disruptive Technology in Digital Public Service Provision-The Current Landscape 9th

Disruptive Technology in Digital Public Service Provision-Th...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Karunaratne, Thashmee Zitnik, Slavko Stockholm Univ Borgarfjordsgatan 12 S-16455 Kista Sweden Univ Ljubljana Vecna Pot 113 Ljubljana 1000 Slovenia

ISBN: (纸本)9789819732982;9789819732999

Digitalization of the processes of public service has been the focus of e-government research for more than decades, but how the provisioning process can be improved using new and disruptive technologies is something that has not been fully comprehended in the related research or practice. This is becoming highly relevant in the present since digital facilities are expanding across borders under the European single digital market initiative including the once-only principle (OOP) and single digital gateway regulation (SDGR). How disruptive technology can improve the quality of cross-border information transfer was a key task in the European Union-funded project Digital Europe for All. However, on the way forward to efficient and effective integration of these technologies into the cross-broader public service provision, a barrier was identified, namely, the lack of knowledge of such applications at local and national levels. This lack of clarity of how and in which ways successful disruptive technologies impact public service urges us to systematically review the status of the methods and applications in contemporary research. Hence this research instruments a systematic review of disruptive technologies in e-government. The results of the analysis of about 400 scientific articles screened through a predefined selection protocol using automatic text mining methods show the trends on topics (keywords) (1) policy development support systems (2) understanding model services systems, (3) smart governance systems citizens, (4) technology policy process develops and (5) health data social policy technology. After closer reading 21 articles that are directly relevant are selected for deeper analysis. The outcome of the literature study showed majority of selected studies are focused on discussions, application proposals, different assessment tools or framework definitions for deploying AI for public services.

关键词： Digital government Public service provision Literature review

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Analysis and Prediction of the Sentiments of the WhatsApp Chat Using Sentiment Analysis 8th

Analysis and Prediction of the Sentiments of the WhatsApp Ch...

引用

8th Smart Trends in Computing and Communications (SmartCom)

作者： Prajapati, Purvi Zaveri, Rushil Shah, Heli Charotar Univ Sci & Technol CHARUSAT Fac Technol & Engn FTE Chandubhai S Patel Inst Technol CSPIT Smt Kundanben Dinsha Patel Dept Informat Technol Changa 388421 Gujarat India

ISBN: (纸本)9789819713226;9789819713233

Sentiment analysis is invaluable for understanding public opinion, especially for social media monitoring, feedback analysis and marketing strategies. With the rise of social media platforms like WhatsApp, billions of users share their thoughts and feelings every day, especially in group chats. This study dives into the fascinating field of how people experience emotions during communication and provides a comprehensive solution through emotion analysis. The "WhatsApp Chat Analyser" web application adapted for this purpose allows you to analyse WhatsApp group chats. Using Python libraries such as NLTK, Matplotlib, Re, Seaborn, Streamlit and Panda, we present a new approach to distinguish individual thoughts and feelings in a valence, arousal and dominance (VAD) framework and classify WhatsApp group messages. WhatsApp text conversations often evoke curiosity, arousal and a sense of control over emotional dimensions within VAD. A standard support vector machine (SVM) model and language classification are used to assess mood, with mood ratings classified based on the VAD scale. Qualitative content analysis forms the core of our data analysis. The results highlight the central role of WhatsApp groups in sharing information, exchanging ideas and discussing issues, with neutral or positive feelings about the chosen topics. WhatsApp chat data is pre-processed, sentiment analysis of individual messages are normalised using the recommended approach to obtain an overall score. This study improves our understanding of emotional dynamics in WhatsApp group chats and highlights the crucial role of the NLTK library in facilitating linguistic analysis of sentiment evaluation.

关键词： WhatsApp Classification algorithms Chats NLTK Machine learning Sentiment analysis

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Enhancing IoT Device Security: A Comparative Analysis of Machine Learning Algorithms for Attack Detection 1

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2nd International Conference on Forthcoming networks and Sustainability in the AIoT Era (FoNeS-AIoT)

作者： Alzahrani, Abdulaziz Alshammari, Abdulaziz Imam Mohammad Ibn Saud Islamic Univ Riyadh Saudi Arabia

ISBN: (数字)9783031628719

ISBN: (纸本)9783031628702;9783031628719

This study sought to compare the effectiveness, efficiency, and scalability of supervised learning algorithms;logistic regression, decision tree, and random forest in IoT networks' attack detection and evaluate the effectiveness of these algorithms in adapting to evolving attack techniques in IoT networks. The study deployed data from a Telecom company encompassing a dataset with a total of 10,000 records and 8 attributes. Furthermore, the dataset comprised both normal and malicious traffic, with 3,000 records classified as attacks and 6,000 records classified as normal traffic. To ensure the creation of reliable and predictive models, a statistical sampling technique called Synthetic Minority OverSampling Technique (SMOTE) was employed. Based on the experiments, the logistic regression algorithm proved to be the most accurate, followed by random forest, and lastly the decision tree algorithm. In the context of IoT device security, the research contributed to an understanding of data preprocessing techniques, feature engineering, and model evaluation. The correlation analysis and heatmap visualization provide valuable insights into the relationships between various variables and highlight potential patterns and trends in the data. This study provides significant knowledge on the improvement of IoT devices' security via machine learning algorithms.

关键词： Machine Learning Logistic Regression Decision Tree Random Forest

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A Comprehensive Overview of Bug Algorithms for Decentralized Mapping and Navigation 8th

A Comprehensive Overview of Bug Algorithms for Decentralized...

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Patil, Aditya Chandolkar, Shreyas Kothawade, Pranit Shaikh, Sohel Jaiswal, Rupesh Patwardhan, Aditya SCTRs Pune Inst Comp Technol Dept Elect & Telecommun Pune Maharashtra India Espressif Syst Pune Maharashtra India

ISBN: (纸本)9789819713226;9789819713233

This paper examines various BUG and SWARM algorithms for robot navigation. We provide a comprehensive summary of initial BUG algorithms, differences, and advancements, along with a comparative study. The context and motivation for different BUG algorithms are provided for navigation in an environment with obstacles and reviews over 20 contributions made in the time frame of 2002 to 2023. These contributions are categorized based on their selection of BUG algorithm, SWARM compatibility, inter-robot detection, etc. We also discuss the real-life requirements of a navigation algorithm and review each work based on said criteria. Possible drawbacks and issues are also discussed.

关键词： BUG algorithm SWARM Mapping Navigation Inter-robot communication Multi-robot systems

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The Ethical Considerations of Using a Machine Learning Algorithm in Cloud Computing 13th

The Ethical Considerations of Using a Machine Learning Algor...

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13th Computer Science Online Conference

作者： Sekwatlakwatla, Sello Prince Malele, Vusumuzi North West Univ Dept Comp Sci & Informat Syst Potchefstroom South Africa

ISBN: (纸本)9783031702846;9783031702853

The use of machine learning algorithms (ML) is automating a wide range of daily decisions. Using machine learning to improve cloud computing resource allocationwith prediction models can transform industries, improve products, and improve everyday lives for consumers. Although machine learning can achieve powerful results, ethical issues persist. This article presents a bibliometric analysis of the prevalence of ethical issues in machine learning. South Africa has 26% of data protection and ethics strategies, while Egypt has 21%. In comparison to developing countries, the United States has 42%, followed by China with 15%. African countries are understudied in the field of machine learning and ethics;however, developing countries are leading the normative debate on machine learning, shaping its outlines, they gradually impose their perspective on the continent without taking into account the diversity of ethical perspectives.

关键词： Machine learning Ethics Regulations

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ONOS SDN Framework: Assessing the Impact of Single and Multi-Controller Architectures on Network Efficiency 8th

ONOS SDN Framework: Assessing the Impact of Single and Multi...

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Abraham, Adarsh Liju Hrishikesh, B. H. Atehar, Md Taseen Hegde, Bhagyashree Narayan Giri, Animesh PES Univ Bengaluru 560100 India

ISBN: (纸本)9789819713226;9789819713233

Software-Defined Networking (SDN) has transformed the computer network management by decoupling the control plane from the data plane, granting immense flexibility, and programmability. Within the SDN world, the Open Network Operating System (ONOS) controller and the Mininet emulator have emerged as pivotal tools. This research paper provides a comprehensive comparative analysis of single-controller and multi-controller architectures within the specific context of ONOS and Mininet, aiming to explore their respective strengths and weaknesses. Single-controller configurations centralize control, offering a simplified network management perspective, while multi-controller setups distribute control across multiple nodes, potentially enhancing scalability and fault tolerance. Our analysis comprises diverse performance metrics, including latency, throughput, and scalability, in addition to considerations such as fault tolerance, load balancing, and the complexity of management and configuration. By presenting the observed evidence derived from experimental setups and real-world use cases, this research paper offers valuable guidance to network administrators, engineers, and researchers with the decision of selecting the most suitable architectural approach for SDN deployments utilizing ONOS and Mininet. Moreover, it provides insights into the current state of SDN technology and a forward-looking perspective on potential advancements and challenges within this dynamic field of technology.

关键词： SDN ONOS Mininet Docker Atomix

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Human Guided Multi-proportions Topic Model for Rare Event Detection Without Using Labels

Human Guided Multi-proportions Topic Model for Rare Event De...

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Intelligent systems Conference

作者： Das, Mrinal Jain, Gaurav Indian Inst Technol Palakkad Palakkad India

ISBN: (纸本)9783031663352;9783031663369

Rare events are subtle in their presence but significant in their impact. However, subtle presence coupled with lack of labels makes it hard to detect rare events. There is no existing method that can detect a predefined rare event without using labels at all. In this paper, we propose a mechanism to employ human guidance in a topic modeling setup to avoid labels, where events are modeled as topics. In contrary to the standard topic models, we use multiple proportions over topics to emphasize rare topics. Following Bayesian principle, we introduce expert information to bias the inference mechanism of the proposed topic model. The expert information required is in the order of number of rare events to be detected which is significantly less compared to the labeling requirement of the order of samples in standard methods. The overall process turns out to be a simple mechanism based on Gibbs sampling. We demonstrate the efficacy of the proposed approach on texts as well as images, and found to achieve significant performance.

关键词： Topic models Rare events Human guidance

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Habitat Suitability Assessment of Three Passerine Birds Using Ensemble Learning with Diverse Models 12th

Habitat Suitability Assessment of Three Passerine Birds Usin...

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12th World Conference on Information systems and Technologies (WorldCIST)

作者： El Alaoui, Omar Idri, Ali Mohammed V Univ Rabat ENSIAS Rabat Morocco Mohammed VI Polytech Univ Benguerir Ben Guerir Morocco

ISBN: (纸本)9783031602207;9783031602214

The application of machine learning (ML) for predicting species habitat suitability has become increasingly popular. However, using a singleML algorithm may not provide optimal predictions for a given dataset, making it challenging to achieve high accuracy. Therefore, this study proposes a novel approach to assess habitat suitability of three redstarts species (P. Moussieri, P. Ochruros, and P. Phoenicurus) based on ensemble learning techniques. Initially, eight ML models namely MLP, SVM, KNN, Decision Trees (DT), Gradient Boosting Classifier (GB), Random Forest (RF), AdaBoost (AB), and Quadratic Discriminant Analysis (QDA) were trained individually. Then, based on the diversity of these base-learners, seven heterogeneous ensembles of two up to eight models were constructed for each species dataset. This study presents a thorough modeling framework for implementing heterogeneous ensembles and enhancing the overall comprehension of their performance in comparison to single models. The performance of this experiment was evaluated using: (1) six performance measures (AUC, sensitivity, specificity, accuracy, Kappa, and TSS), (2) Borda Count ranking method, and (3) Scott Knott statistical test. Results showed the potential of heterogeneous ensembles for predicting habitat suitability of the three redstarts birds. The proposed ensembles consistently outperformed single models across all datasets.

关键词： heterogenous ensemble learning species distribution modeling ecological niche models habitat suitability machine learning redstart birds

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A WSN and IoT-Based Wire Consumption Monitoring System for Mobile Welding Machines 16th

A WSN and IoT-Based Wire Consumption Monitoring System for M...

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16th International Conference on Ubiquitous Computing and Ambient Intelligence

作者： Mirani, Akseer Ali Awasthi, Anshul O'Mahony, Niall Walsh, Joseph Munster Technol Univ IMaR Res Ctr Tralee V92 CX88 Ireland CONFIRM Res Ctr Unit Pk Point 2 Dublin Rd Limerick V94 C928 Ireland

ISBN: (纸本)9783031775703;9783031775710

The convergence of Wireless Sensor networks (WSN) and the Internet of Things (IoT) technology in industrial applications brings remarkable innovations in the Industry 4.0 domain, resulting in improved industrial processes with better efficiency and productivity. This technological integration enables the remote monitoring and controlling of mobile industrial assets, and the welding machine is one asset that benefits from this technology. In the contemporary world, the Metal Inert Gas (MIG) welding process is one of the standard techniques employed to join metal pieces in industries by utilizing consumable wire, resulting in satisfactory welding outcomes. However, modern industrial systems using this technique lack the ability to monitor wire consumption, which can help track machines' operational activities and manage the supply chain process. In this project, we design, develop, and implement a WSN and IoT-based system to monitor the wire consumption of a mobile welding machine on an indoor factory floor using edge and cloud user interfaces. In this article, we first discuss each component's hardware and software development in the proposed layered architecture, followed by the results obtained from our research during the evaluation and implementation phases. Finally, we conclude our research by identifying limitations and possible directions for future work.

关键词： Welding Wire Welding Process Wireless Sensor Neworks Internet of Things Industry 4.0 Edge User Interface

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